Modern reading instruction recommends students to be matched to their individually assessed reading level and therefore the determination of textual complexity of educational material is now a requirement for educators to exactly match readers to the texts of their ability level. There are currently two general methods for determining the complexity of a text: quantitatively measuring features that have been proposed to have a strong influence on its complexity, or qualitatively inferring the complexity via subjective human experience.
Both of these mechanisms are heavily criticized by educational professionals for falling short in several areas. First, current quantitative analysis tends to either over or under level particular pieces of text. Second, current qualitative analysis is highly subjective, not repeatable, and takes a significant amount of time to perform.
The present disclosure is directed toward, but not limited to, improving the above noted problems by combining the benefits of quantitative analysis and qualitative analysis with a machine learning algorithm to determine the complexity of a given piece of text.